10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm
5⃣ Numpy is another Python library that you will use every single day.

@keithgalli's "Complete Python NumPy Tutorial" is a great start.

https://t.co/Xg0YbuR8fz
6⃣ One of the most basic algorithms that you can learn is Decision Trees.

Watch @random_forests' video where he builds a decision tree from scratch:

https://t.co/tKtUpO1K3l
7⃣ It's hard to talk about machine learning without touching on neural networks.

Probably the best video out there that explains how neural networks work is @3blue1brown's:

https://t.co/OMJHiG7PIu
8⃣ Scikit-Learn is one of the most popular machine learning libraries out there.

@simplilearn's "Scikit-Learn Tutorial" is a great place to start.

https://t.co/efd1kmz07c
9⃣ TensorFlow is the most popular deep learning library that's currently used in the industry.

Here is a massive 7-hour tutorial of TensorFlow 2.0 produced by @freeCodeCamp.

https://t.co/BYUoAQJEeu
🔟 Finally, a great way to start getting familiar with machine learning is the bite-sized recipes published by Google.

This series is worth every minute.

Playlist: https://t.co/xDqhmNQoWg
If you are looking for real-life, hands-on information related to machine learning, follow me.

✌️

If you have questions or suggestions about topics you'd like to hear about, let me know.

More from Santiago

More from Machine learning

Really enjoyed digging into recent innovations in the football analytics industry.

>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇‍♂️ https://t.co/9YOSrl8TdN


For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method
https://t.co/Hx8XTUMpJ5


Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN


So many use-cases:
1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.
2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?

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"I really want to break into Product Management"

make products.

"If only someone would tell me how I can get a startup to notice me."

Make Products.

"I guess it's impossible and I'll never break into the industry."

MAKE PRODUCTS.

Courtesy of @edbrisson's wonderful thread on breaking into comics –
https://t.co/TgNblNSCBj – here is why the same applies to Product Management, too.


There is no better way of learning the craft of product, or proving your potential to employers, than just doing it.

You do not need anybody's permission. We don't have diplomas, nor doctorates. We can barely agree on a single standard of what a Product Manager is supposed to do.

But – there is at least one blindingly obvious industry consensus – a Product Manager makes Products.

And they don't need to be kept at the exact right temperature, given endless resource, or carefully protected in order to do this.

They find their own way.